Source code for ax.models.torch.cbo_sac

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Any, Dict, List, Optional

from ax.core.search_space import SearchSpaceDigest
from ax.core.types import TCandidateMetadata
from ax.models.torch.botorch import BotorchModel
from ax.models.torch_base import TorchModel
from ax.utils.common.docutils import copy_doc
from ax.utils.common.logger import get_logger
from import fit_gpytorch_model
from botorch.models.contextual import SACGP
from botorch.models.gpytorch import GPyTorchModel
from botorch.models.model_list_gp_regression import ModelListGP
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from torch import Tensor

logger = get_logger(__name__)

[docs]class SACBO(BotorchModel): """Does Bayesian optimization with structural additive contextual GP (SACGP). The parameter space decomposition must be provided. Args: decomposition: Keys are context names. Values are the lists of parameter names belong to the context, e.g. {'context1': ['p1_c1', 'p2_c1'],'context2': ['p1_c2', 'p2_c2']}. """ def __init__(self, decomposition: Dict[str, List[str]]) -> None: # add validation for input decomposition for param_list in decomposition.values(): assert len(param_list) == len( list(decomposition.values())[0] ), "Each Context must contain same number of parameters" self.decomposition = decomposition self.feature_names: List[str] = [] super().__init__(model_constructor=self.get_and_fit_model)
[docs] @copy_doc( def fit( self, Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], search_space_digest: SearchSpaceDigest, metric_names: List[str], candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None, ) -> None: if len(search_space_digest.feature_names) == 0: raise ValueError("feature names are required for SACBO") self.feature_names = search_space_digest.feature_names super().fit( Xs=Xs, Ys=Ys, Yvars=Yvars, search_space_digest=search_space_digest, metric_names=metric_names, )
[docs] def get_and_fit_model( self, Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], task_features: List[int], fidelity_features: List[int], metric_names: List[str], state_dict: Optional[Dict[str, Tensor]] = None, fidelity_model_id: Optional[int] = None, **kwargs: Any, ) -> GPyTorchModel: """Get a fitted StructuralAdditiveContextualGP model for each outcome. Args: Xs: X for each outcome. Ys: Y for each outcome. Yvars: Noise variance of Y for each outcome. Returns: Fitted StructuralAdditiveContextualGP model. """ # generate model space decomposition dict decomp_index = generate_model_space_decomposition( decomposition=self.decomposition, feature_names=self.feature_names ) models = [] for i, X in enumerate(Xs): Yvar = Yvars[i].clamp_min_(MIN_OBSERVED_NOISE_LEVEL) gp_m = SACGP(X, Ys[i], Yvar, decomp_index) mll = ExactMarginalLogLikelihood(gp_m.likelihood, gp_m) fit_gpytorch_model(mll) models.append(gp_m) if len(models) == 1: model = models[0] else: model = ModelListGP(*models)[0]) return model
[docs]def generate_model_space_decomposition( decomposition: Dict[str, List[str]], feature_names: List[str] ) -> Dict[str, List[int]]: # validate input decomposition for param_list in decomposition.values(): for param in param_list: assert ( param in feature_names ), f"cannot find parameter {param} in search space" # generate parameter index list align with the input arrays decomp_index = {} for context, param_names in decomposition.items(): decomp_index[context] = [feature_names.index(p) for p in param_names] return decomp_index